We propose three evaluation metrics for comparing the explanations generated by different approaches. Local accuracy is defined as the mean square error between the aggregated explanations generated by the model explanation approach and the probabilistic outputs of the original predictive model. We note that there is no local accuracy evaluation for RAIM, since it is not an additive feature attribution method. Local accuracy reflects how accurate the summed explanation fits to the predicted output. Faithfulness is achieved by evaluating the area over the most relevant first perturbation curve (AOPC), which assesses the ability of model assigning high values to those input variables that have the true high influence to the final predictive outcomes [1]}. In our evaluation, we assess the AOPC of model explanation approaches at different cutoff points along the rank of feature importance. We also report the AOPC of top 2,000 data points ranked by the model explanation methods. Stability is defined as the extent of changes in explanation when applying small perturbation to the input that does not change the predictive outcome. In our evaluation, we use the estimated Lipschitz continuity [2]} to quantify the stability of the explanation. The explanations generated with smaller estimated Lipschitz continuity should be more stable.
